Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0)

Land surface models used in climate models neglect the roughness sublayer and parameterize within-canopy turbulence in an ad hoc manner. We implemented a roughness sublayer turbulence parameterization in a multilayer canopy model (CLM-ml v0) to test if this theory provides a tractable parameterization extending from the ground through the canopy and the roughness sublayer. We compared the canopy model with the Community Land Model (CLM4.5) at seven forest, two grassland, and three cropland AmeriFlux sites over a range of canopy heights, leaf area indexes, and climates. CLM4.5 has pronounced biases during summer months at forest sites in midday latent heat flux, sensible heat flux, gross primary production, nighttime friction velocity, and the radiative temperature diurnal range. The new canopy model reduces these biases by introducing new physics. Advances in modeling stomatal conductance and canopy physiology beyond what is in CLM4.5 substantially improve model performance at the forest sites. The signature of the roughness sublayer is most evident in nighttime friction velocity and the diurnal cycle of radiative temperature, but is also seen in sensible heat flux. Within-canopy temperature profiles are markedly different compared with profiles obtained using Monin–Obukhov similarity theory, and the roughness sublayer produces cooler daytime and warmer nighttime temperatures. The herbaceous sites also show model improvements, but the improvements are related less systematically to the roughness sublayer parameterization in these canopies. The multilayer canopy with the roughness sublayer turbulence improves simulations compared with CLM4.5 while also advancing the theoretical basis for surface flux parameterizations

The Community Land Model version 5 : description of new features, benchmarking, and impact of forcing uncertainty

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time‐evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5

Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands

Surficial enhanced rock weathering (ERW) is a land-based carbon dioxide removal (CDR) strategy that involves applying crushed silicate rock (e.g., basalt) to agricultural soils. However, unintended biogeochemical interactions with the nitrogen cycle may arise through ERW increasing soil pH as basalt grains undergo dissolution that may reinforce, counteract, or even offset the climate benefits from carbon sequestration. Increases in soil pH could drive changes in the soil emissions of key non-CO2 greenhouse gases, e.g., nitrous oxide (N2O), and trace gases, e.g., nitric oxide (NO) and ammonia (NH3), that affect air quality and crop and human health. We present the development and implementation of a new improved nitrogen cycling scheme for the Community Land Model v5 (CLM5), the land component of the Community Earth System Model, allowing evaluation of ERW effects on soil gas emissions. We base the new parameterizations on datasets derived from soil pH responses of N2O, NO, and NH3 in ERW field trial and mesocosm experiments with crushed basalt. These new capabilities involve the direct implementation of routines within the CLM5 N cycle framework, along with asynchronous coupling of soil pH changes estimated through an ERW model. We successfully validated simulated “control” (i.e., no ERW) seasonal cycles of soil N2O, NO, and NH3 emissions against a wide range of global emission inventories. We benchmark simulated mitigation of soil N2O fluxes in response to ERW against a subset of data from ERW field trials in the US Corn Belt. Using the new scheme, we provide a specific example of the effect of large-scale ERW deployment with croplands on soil nitrogen fluxes across five key regions with high potential for CDR with ERW (North America, Brazil, Europe, India, and China). Across these regions, ERW implementation led to marked reductions in N2O and NO (both 18 %), with moderate increases in NH3 (2 %). While further developments are still required in our implementations when additional ERW data become available, our improved N cycle scheme within CLM5 has utility for investigating the potential of ERW point-source and regional effects of soil N2O, NO, and NH3 fluxes in response to current and future climates. This framework also provides the basis for assessing the implications of ERW for air quality given the role of NO in tropospheric ozone formation, as well as both NO and NH3 in inorganic aerosol formation.</p

Using real-time observations and land surface modelling for improved irrigation and water resources management in Mediterranean climate

Irrigated agriculture is essential to sustain crop production and livelihoods of the rural population in semi-arid and arid regions such as the Mediterranean. Meanwhile, unsustainable irrigation practices, population growth, and climate change are increasing agricultural water demand while exacerbating water scarcity. Effective measures to reduce agricultural water consumption while sustaining a high level of crop production and securing environmental sustainability are therefore urgently needed. This thesis aims at increasing the availability of environmental data and advancing the representation of agricultural systems in land surface models to improve local and regional scale irrigation and water resources management in the Mediterranean. In the first part, the use of a low-cost weather station to deliver reliable and timely data for environmental monitoring, research, and modelling is assessed. Performance and data quality of multiple stations are examined in terms of inter-sensor variability and in comparison to a high-performance weather station. The second part of this thesis focusses on improving the representation of typical Mediterranean crops in the Community Land Model version 5. A new sub-model to model deciduous fruit orchards is developed encompassing crop phenological stages, biomass growth and partitioning into different plant organs as well as typical management practices. The development is then tested using extensive field measurements from an apple orchard. Finally, the new sub-model is used to assess irrigation and water management in a small Greek catchment dominated by irrigated apple orchards. First, simulated crop growth and soil moisture dynamics are examined in relation to irrigation and compared to observations from two monitored apple orchards. Thereby, further model improvements are made to represent the local irrigation practices. Subsequently, the model is applied at regional scale to determine irrigation requirements and examine the impact of different irrigation deficit scenarios on yield and crop water use efficiency as well as to assess the water saving potential in the catchment

Vulnerability of Primary Productivity and Its Carbon Use Efficiency to Unfavorable Climatic Conditions in Jambi Province, Indonesia

Climatic conditions and land cover play crucial roles in influencing the process of carbon uptake through vegetation. This study aimed to analyze the effect of climate variability on carbon uptake of four different land covers in Jambi Province, Indonesia. The four land cover types studied were: forest, shrub, grass, and irrigated soybean, based on Community Land Model version 5. Forest was found to have the highest net primary production (NPP) compared to the other land covers. Seasonal climate variability showed no major effect on NPP and gross primary production (GPP). However, GPP and NPP experienced significant declines during El Niño Southern Oscillation (ENSO), particularly in 2015. Carbon use efficiency (CUE = NPP/GPP) was also affected by ENSO, where CUE decreased during El Niño, particularly in October and November with an increased number of days without rainfall. In addition, the difference between latent (LE) and sensible heat (H) flux, denoted as (LE-H), decreased from August to November. This difference was highly correlated with NPP. This result indicates that when water supply is low, stomata will close, thereby reducing photosynthesis and transpiration, and allocating more of the available energy to sensible heat flux rather than latent heat flux

Influence of Vertical Heterogeneities in the Canopy Microenvironment on Interannual Variability of Carbon Uptake in Temperate Deciduous Forests

Vegetation structure and function are key design choices in terrestrial models that affect the relationship between carbon uptake and environmental drivers. Here, we investigate how representing canopy vertical structure in a terrestrial biosphere model- that is, micrometeorological, leaf area, and leaf water profiles- influences carbon uptake at five U.S. temperate deciduous forest sites in July. Specifically, we test whether the interannual variability (IAV) of gross primary productivity (GPP) responds differently to four abiotic environmental drivers- air temperature, relative humidity, incoming shortwave radiation, and soil moisture- using either a Community Land Model multilayer canopy model (CLM- ml) or a big- leaf model (CLM4.5/CLM5). We conclude that vertical leaf area and microclimatic profiles (temperature, humidity, and wind) do not impact GPP IAV compared to a single- layer model when plant hydraulics is excluded. However, with a mechanistic representation of plant hydraulics there is vertically varying water stress in CLM- ml, and the sensitivity of carbon uptake to particular climate variables changes with height, resulting in dampened canopy- scale GPP IAV relative to CLM4.5. Dampening is due to both a reduced dependence on soil moisture and opposing climatic forcing on different leaf layers. Such dampening is not evident in the single- layer representation of plant hydraulic water stress implemented in the recently released CLM5. Overall, both model representations of the canopy fail to accurately simulate observed GPP IAV and this may be related by their inability to capture the upper range of observed hourly GPP and diffuse light- GPP relationships that cannot be resolved by canopy structure alone.Key PointsExplicitly simulated leaf area and microclimatic profiles do not affect gross primary productivity (GPP) interannual variability compared to a - big- leaf- simplificationMultilayer plant hydraulics lead to vertically varying water stress, altering leaf- layer responses to interannual climate variationsAll model simulations underestimate hourly GPP compared to FLUXNET estimates, adversely impacting simulated GPP interannual variabilityPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156484/2/jgrg21710_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156484/1/jgrg21710.pd

Implementing a new rubber plant functional type in the Community Land Model (CLM5) improves accuracy of carbon and water flux estimation

Rubber plantations are an economically viable land-use type that occupies large swathes of land in Southeast Asia that have undergone conversion from native forest to intensive plantation forestry. Such land-use change has a strong impact on carbon, energy, and water fluxes in ecosystems, and uncertainties exist in the modeling of future land-use change impacts on these fluxes due to the scarcity of measured data and poor representation of key biogeochemical processes. In this current modeling effort, we utilized the Community Land Model Version 5 (CLM5) to simulate a rubber plant functional type (PFT) by comparing the baseline parameter values of tropical evergreen PFT and tropical deciduous PFT with a newly developed rubber PFT (focused on the parameterization and modification of phenology and allocation processes) based on site-level observations of a rubber clone in Indonesia. We found that the baseline tropical evergreen and baseline tropical deciduous functions and parameterizations in CLM5 poorly simulate the leaf area index, carbon dynamics, and water fluxes of rubber plantations. The newly developed rubber PFT and parametrizations (CLM-rubber) showed that daylength could be used as a universal trigger for defoliation and refoliation of rubber plantations. CLM-rubber was able to predict seasonal patterns of latex yield reasonably well, despite highly variable tapping periods across Southeast Asia. Further, model comparisons indicated that CLM-rubber can simulate carbon and energy fluxes similar to the existing rubber model simulations available in the literature. Our modeling results indicate that CLM-rubber can be applied in Southeast Asia to examine variations in carbon and water fluxes for rubber plantations and assess how rubber-related land-use changes in the tropics feedback to climate through carbon and water cycling

Vulnerability of Primary Productivity and Its Carbon Use Efficiency to Unfavorable Climatic Conditions in Jambi Province, Indonesia

Climatic conditions and land cover play crucial roles in influencing the process of carbon uptake through vegetation. This study aimed to analyze the effect of climate variability on carbon uptake of four different land covers in Jambi Province, Indonesia. The four land cover types studied were: forest, shrub, grass, and irrigated soybean, based on Community Land Model version 5. Forest was found to have the highest net primary production (NPP) compared to the other land covers. Seasonal climate variability showed no major effect on NPP and gross primary production (GPP). However, GPP and NPP experienced significant declines during El Niño Southern Oscillation (ENSO), particularly in 2015. Carbon use efficiency (CUE = NPP/GPP) was also affected by ENSO, where CUE decreased during El Niño, particularly in October and November with an increased number of days without rainfall. In addition, the difference between latent (LE) and sensible heat (H) flux, denoted as (LE-H), decreased from August to November. This difference was highly correlated with NPP. This result indicates that when water supply is low, stomata will close, thereby reducing photosynthesis and transpiration, and allocating more of the available energy to sensible heat flux rather than latent heat flux

Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach

Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.</p

Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data

Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades.We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56% to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.LIFE programme of the European Union under contract number LIFE 17 CCA/ES/000063, with additional funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1502/1-2022 – project number 45005826